{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network Traffic AutoTSEstimator (customized model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In telco, accurate forecast of KPIs (e.g. network traffic, utilizations, user experience, etc.) for communication networks ( 2G/3G/4G/5G/wired) can help predict network failures, allocate resource, or save energy. \n",
    "\n",
    "In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demonstrate how to use **AutoTSEstimator** to adjust the parameters of a customized model.\n",
    "\n",
    "For demonstration, we use the publicly available network traffic data repository maintained by the [WIDE project](http://mawi.wide.ad.jp/mawi/) and in particular, the network traffic traces aggregated every 2 hours (i.e. AverageRate in Mbps/Gbps and Total Bytes) in year 2018 and 2019 at the transit link of WIDE to the upstream ISP ([dataset link](http://mawi.wide.ad.jp/~agurim/dataset/)). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section defines some helper functions to be used in the following procedures. You can refer to it later when they're used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def plot_predict_actual_values(date, y_pred, y_test, ylabel):\n",
    "    \"\"\"\n",
    "    plot the predicted values and actual values (for the test data)\n",
    "    \"\"\"\n",
    "    fig, axs = plt.subplots(figsize=(16, 6))\n",
    "\n",
    "    axs.plot(date, y_pred, color='red', label='predicted values')\n",
    "    axs.plot(date, y_test, color='blue', label='actual values')\n",
    "    axs.set_title('the predicted values and actual values (for the test data)')\n",
    "\n",
    "    plt.xlabel('test datetime')\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `get_public_tsdata` to load and process the data, specify the name and path, and the ts data set will be returned. Currently we support `nyc_taxi`, `network_traffic`, `fsi`, `AIOps`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from bigdl.chronos.data import get_public_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'network_traffic'\n",
    "\n",
    "stand = StandardScaler()\n",
    "tsdata_train, tsdata_val, \\\n",
    "    tsdata_test = get_public_dataset(name,\n",
    "                                     with_split=True,\n",
    "                                     val_ratio=0.1,\n",
    "                                     test_ratio=0.1)\n",
    "for tsdata in [tsdata_train, tsdata_val, tsdata_test]:\n",
    "    tsdata.impute('last')\\\n",
    "          .scale(stand, fit=tsdata is tsdata_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regarding the model, we use torch.nn.GRU to define, More details, please refer to [here](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html?highlight=gru#torch.nn.GRU).\n",
    "\n",
    "```python\n",
    "# The input and output data should be reshaped to a 3d numpy array with the format of (batchsize, seq_len, feature_num).\n",
    "out = out.view(out.shape[0], 1, out.shape[-1])\n",
    "```\n",
    " Detailed information please refer to [here](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/data_processing_feature_engineering.html#roll-sampling)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "class GRUNet(nn.Module):\n",
    "    def __init__(self, input_dim, layer_num, hidden_dim, dropout, output_dim):\n",
    "        super(GRUNet, self).__init__()\n",
    "        self.layer_num = layer_num\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.gru = nn.GRU(input_dim, hidden_dim, layer_num, dropout=dropout, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        h0 = torch.randn(self.layer_num, input_seq.size(0), self.hidden_dim)\n",
    "        gru_out, _ = self.gru(input_seq, h0)\n",
    "        out = self.fc(gru_out[:, -1, :])\n",
    "        out = out.view(out.shape[0], 1, out.shape[-1])\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a model creator function which returns the defined model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_creator(config):\n",
    "    return GRUNet(input_dim=config['input_feature_num'],\n",
    "                  layer_num=config['layer_num'],\n",
    "                  hidden_dim=config['hidden_dim'],\n",
    "                  dropout=config['dropout'],\n",
    "                  output_dim=config['output_feature_num'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing orca context\n",
      "Current pyspark location is : /home/liangs/spark/python/lib/pyspark.zip/pyspark/__init__.py\n",
      "Start to getOrCreate SparkContext\n",
      "pyspark_submit_args is:  --driver-class-path /home/liangs/BigDL/dist/lib/bigdl-dllib-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar:/home/liangs/BigDL/dist/lib/bigdl-orca-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar pyspark-shell \n",
      "Successfully got a SparkContext\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-10 22:11:27,183\tINFO services.py:1340 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://10.239.44.108:8265\u001b[39m\u001b[22m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'node_ip_address': '10.239.44.108', 'raylet_ip_address': '10.239.44.108', 'redis_address': '10.239.44.108:6379', 'object_store_address': '/tmp/ray/session_2022-01-10_22-11-24_778075_18140/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-01-10_22-11-24_778075_18140/sockets/raylet', 'webui_url': '10.239.44.108:8265', 'session_dir': '/tmp/ray/session_2022-01-10_22-11-24_778075_18140', 'metrics_export_port': 52330, 'node_id': '678513955776bf86b3172c4c00e36db93813ca31117f649cd5a994b3'}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://rose-X299-AORUS-Gaming-3-Pro.sh.intel.com:4040\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v2.4.3</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[4]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>pyspark-shell</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "<SparkContext master=local[4] appName=pyspark-shell>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bigdl.orca import init_orca_context\n",
    "init_orca_context(cores=4, memory='10g', init_ray_on_spark=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Search space & Initialize `AutoTSEstimator`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the search interval of the parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bigdl.orca.automl import hp\n",
    "search_space={\n",
    "    'hidden_dim': hp.grid_search([32, 64]),\n",
    "    'layer_num': hp.grid_search([2, 4]),\n",
    "    'dropout': hp.uniform(0.1, 0.2)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pass the defined search space and model to `AutoTSEstimator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bigdl.chronos.autots.autotsestimator import AutoTSEstimator\n",
    "autotsest = AutoTSEstimator(model=model_creator,\n",
    "                            search_space=search_space,\n",
    "                            past_seq_len=15,\n",
    "                            metric='mse',\n",
    "                            loss=torch.nn.MSELoss(),\n",
    "                            cpus_per_trial=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit Model & Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-10 22:11:28,579\tWARNING function_runner.py:562 -- Function checkpointing is disabled. This may result in unexpected behavior when using checkpointing features or certain schedulers. To enable, set the train function arguments to be `func(config, checkpoint_dir=None)`.\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m \n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m User settings:\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m \n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_AFFINITY=granularity=fine,compact,1,0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_BLOCKTIME=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_DUPLICATE_LIB_OK=True\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_INIT_AT_FORK=FALSE\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_SETTINGS=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_NUM_THREADS=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m \n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m Effective settings:\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m \n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_ABORT_DELAY=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_ADAPTIVE_LOCK_PROPS='1,1024'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_ALIGN_ALLOC=64\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_ALL_THREADPRIVATE=128\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_ATOMIC_MODE=2\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_BLOCKTIME=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_CPUINFO_FILE: value is not defined\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_DETERMINISTIC_REDUCTION=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_DEVICE_THREAD_LIMIT=2147483647\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_DISP_HAND_THREAD=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_DISP_NUM_BUFFERS=7\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_DUPLICATE_LIB_OK=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_ENABLE_TASK_THROTTLING=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_FORCE_MONOTONIC_DYNAMIC_SCHEDULE=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_FORCE_REDUCTION: value is not defined\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_FOREIGN_THREADS_THREADPRIVATE=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_FORKJOIN_BARRIER='2,2'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_FORKJOIN_BARRIER_PATTERN='hyper,hyper'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_FORKJOIN_FRAMES=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_FORKJOIN_FRAMES_MODE=3\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_GTID_MODE=3\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_HANDLE_SIGNALS=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_HOT_TEAMS_MAX_LEVEL=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_HOT_TEAMS_MODE=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_INIT_AT_FORK=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_ITT_PREPARE_DELAY=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_LIBRARY=throughput\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_LOCK_KIND=tas\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_MALLOC_POOL_INCR=1M\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_MWAIT_HINTS=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_NESTING_MODE=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_NUM_LOCKS_IN_BLOCK=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_PLAIN_BARRIER='2,2'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_PLAIN_BARRIER_PATTERN='hyper,hyper'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_REDUCTION_BARRIER='1,1'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_REDUCTION_BARRIER_PATTERN='hyper,hyper'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_SCHEDULE='static,balanced;guided,iterative'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_SETTINGS=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_SPIN_BACKOFF_PARAMS='4096,100'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_STACKOFFSET=64\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_STACKPAD=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_STACKSIZE=8M\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_STORAGE_MAP=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_TASKING=2\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_TASKLOOP_MIN_TASKS=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_TASK_STEALING_CONSTRAINT=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_TEAMS_PROC_BIND=spread\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_TEAMS_THREAD_LIMIT=20\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_TOPOLOGY_METHOD=all\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_USER_LEVEL_MWAIT=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_USE_YIELD=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_VERSION=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_WARNINGS=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    LIBOMP_NUM_HIDDEN_HELPER_THREADS=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    LIBOMP_USE_HIDDEN_HELPER_TASK=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_AFFINITY_FORMAT='OMP: pid %P tid %i thread %n bound to OS proc set {%A}'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_ALLOCATOR=omp_default_mem_alloc\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_CANCELLATION=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_DEBUG=disabled\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_DEFAULT_DEVICE=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_DISPLAY_AFFINITY=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_DISPLAY_ENV=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_DYNAMIC=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_MAX_ACTIVE_LEVELS=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_MAX_TASK_PRIORITY=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_NESTED: deprecated; max-active-levels-var=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_NUM_TEAMS=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_NUM_THREADS='1'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_PLACES='threads'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_PROC_BIND='intel'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_SCHEDULE='static'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_STACKSIZE=8M\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_TARGET_OFFLOAD=DEFAULT\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_TEAMS_THREAD_LIMIT=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_THREAD_LIMIT=2147483647\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_TOOL=enabled\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_TOOL_LIBRARIES: value is not defined\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_TOOL_VERBOSE_INIT: value is not defined\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    OMP_WAIT_POLICY=PASSIVE\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m    KMP_AFFINITY='noverbose,warnings,respect,granularity=thread,compact,1,0'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18469)\u001b[0m \n"
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     "data": {
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       "== Status ==<br>Current time: 2022-01-10 22:11:30 (running for 00:00:01.91)<br>Memory usage on this node: 4.5/125.5 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 2.0/4 CPUs, 0/0 GPUs, 0.0/76.72 GiB heap, 0.0/36.87 GiB objects<br>Result logdir: /tmp/autots_estimator/autots_estimator<br>Number of trials: 4/4 (3 PENDING, 1 RUNNING)<br><table>\n",
       "<thead>\n",
       "<tr><th>Trial name            </th><th>status  </th><th>loc                </th><th style=\"text-align: right;\">  batch_size</th><th style=\"text-align: right;\">  dropout</th><th style=\"text-align: right;\">  hidden_dim</th><th style=\"text-align: right;\">  layer_num</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>train_func_34220_00000</td><td>RUNNING </td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.114795</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          2</td></tr>\n",
       "<tr><td>train_func_34220_00001</td><td>PENDING </td><td>                   </td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.174854</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          2</td></tr>\n",
       "<tr><td>train_func_34220_00002</td><td>PENDING </td><td>                   </td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\"> 0.119272</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          4</td></tr>\n",
       "<tr><td>train_func_34220_00003</td><td>PENDING </td><td>                   </td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.123372</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          4</td></tr>\n",
       "</tbody>\n",
       "</table><br><br>"
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      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m \n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m User settings:\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m \n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_AFFINITY=granularity=fine,compact,1,0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_BLOCKTIME=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_DUPLICATE_LIB_OK=True\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_INIT_AT_FORK=FALSE\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_SETTINGS=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_NUM_THREADS=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m \n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m Effective settings:\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m \n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_ABORT_DELAY=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_ADAPTIVE_LOCK_PROPS='1,1024'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_ALIGN_ALLOC=64\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_ALL_THREADPRIVATE=128\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_ATOMIC_MODE=2\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_BLOCKTIME=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_CPUINFO_FILE: value is not defined\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_DETERMINISTIC_REDUCTION=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_DEVICE_THREAD_LIMIT=2147483647\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_DISP_HAND_THREAD=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_DISP_NUM_BUFFERS=7\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_DUPLICATE_LIB_OK=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_ENABLE_TASK_THROTTLING=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_FORCE_MONOTONIC_DYNAMIC_SCHEDULE=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_FORCE_REDUCTION: value is not defined\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_FOREIGN_THREADS_THREADPRIVATE=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_FORKJOIN_BARRIER='2,2'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_FORKJOIN_BARRIER_PATTERN='hyper,hyper'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_FORKJOIN_FRAMES=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_FORKJOIN_FRAMES_MODE=3\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_GTID_MODE=3\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_HANDLE_SIGNALS=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_HOT_TEAMS_MAX_LEVEL=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_HOT_TEAMS_MODE=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_INIT_AT_FORK=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_ITT_PREPARE_DELAY=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_LIBRARY=throughput\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_LOCK_KIND=tas\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_MALLOC_POOL_INCR=1M\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_MWAIT_HINTS=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_NESTING_MODE=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_NUM_LOCKS_IN_BLOCK=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_PLAIN_BARRIER='2,2'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_PLAIN_BARRIER_PATTERN='hyper,hyper'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_REDUCTION_BARRIER='1,1'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_REDUCTION_BARRIER_PATTERN='hyper,hyper'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_SCHEDULE='static,balanced;guided,iterative'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_SETTINGS=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_SPIN_BACKOFF_PARAMS='4096,100'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_STACKOFFSET=64\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_STACKPAD=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_STACKSIZE=8M\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_STORAGE_MAP=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_TASKING=2\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_TASKLOOP_MIN_TASKS=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_TASK_STEALING_CONSTRAINT=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_TEAMS_PROC_BIND=spread\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_TEAMS_THREAD_LIMIT=20\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_TOPOLOGY_METHOD=all\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_USER_LEVEL_MWAIT=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_USE_YIELD=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_VERSION=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_WARNINGS=true\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    LIBOMP_NUM_HIDDEN_HELPER_THREADS=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    LIBOMP_USE_HIDDEN_HELPER_TASK=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_AFFINITY_FORMAT='OMP: pid %P tid %i thread %n bound to OS proc set {%A}'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_ALLOCATOR=omp_default_mem_alloc\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_CANCELLATION=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_DEBUG=disabled\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_DEFAULT_DEVICE=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_DISPLAY_AFFINITY=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_DISPLAY_ENV=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_DYNAMIC=false\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_MAX_ACTIVE_LEVELS=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_MAX_TASK_PRIORITY=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_NESTED: deprecated; max-active-levels-var=1\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_NUM_TEAMS=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_NUM_THREADS='1'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_PLACES='threads'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_PROC_BIND='intel'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_SCHEDULE='static'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_STACKSIZE=8M\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_TARGET_OFFLOAD=DEFAULT\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_TEAMS_THREAD_LIMIT=0\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_THREAD_LIMIT=2147483647\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_TOOL=enabled\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_TOOL_LIBRARIES: value is not defined\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_TOOL_VERBOSE_INIT: value is not defined\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    OMP_WAIT_POLICY=PASSIVE\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m    KMP_AFFINITY='noverbose,warnings,respect,granularity=thread,compact,1,0'\n",
      "\u001b[2m\u001b[36m(bundle_reservation_check_func pid=18471)\u001b[0m \n"
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     "data": {
      "text/html": [
       "== Status ==<br>Current time: 2022-01-10 22:11:33 (running for 00:00:05.35)<br>Memory usage on this node: 4.7/125.5 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 4.0/4 CPUs, 0/0 GPUs, 0.0/76.72 GiB heap, 0.0/36.87 GiB objects<br>Current best trial: 34220_00000 with mse=0.14968125647653732 and parameters={'hidden_dim': 32, 'layer_num': 2, 'dropout': 0.11479529531584376, 'past_seq_len': 15, 'future_seq_len': 1, 'input_feature_num': None, 'output_feature_num': 2, 'selected_features': [], 'batch_size': 64}<br>Result logdir: /tmp/autots_estimator/autots_estimator<br>Number of trials: 4/4 (2 PENDING, 2 RUNNING)<br><table>\n",
       "<thead>\n",
       "<tr><th>Trial name            </th><th>status  </th><th>loc                </th><th style=\"text-align: right;\">  batch_size</th><th style=\"text-align: right;\">  dropout</th><th style=\"text-align: right;\">  hidden_dim</th><th style=\"text-align: right;\">  layer_num</th><th style=\"text-align: right;\">  iter</th><th style=\"text-align: right;\">  total time (s)</th><th style=\"text-align: right;\">  best_mse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>train_func_34220_00000</td><td>RUNNING </td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.114795</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     1</td><td style=\"text-align: right;\">         1.78467</td><td style=\"text-align: right;\">  0.149681</td><td style=\"text-align: right;\">0.149681</td></tr>\n",
       "<tr><td>train_func_34220_00001</td><td>RUNNING </td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.174854</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">      </td><td style=\"text-align: right;\">                </td><td style=\"text-align: right;\">          </td><td style=\"text-align: right;\">        </td></tr>\n",
       "<tr><td>train_func_34220_00002</td><td>PENDING </td><td>                   </td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\"> 0.119272</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">      </td><td style=\"text-align: right;\">                </td><td style=\"text-align: right;\">          </td><td style=\"text-align: right;\">        </td></tr>\n",
       "<tr><td>train_func_34220_00003</td><td>PENDING </td><td>                   </td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.123372</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">      </td><td style=\"text-align: right;\">                </td><td style=\"text-align: right;\">          </td><td style=\"text-align: right;\">        </td></tr>\n",
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       "== Status ==<br>Current time: 2022-01-10 22:11:39 (running for 00:00:10.50)<br>Memory usage on this node: 4.7/125.5 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 4.0/4 CPUs, 0/0 GPUs, 0.0/76.72 GiB heap, 0.0/36.87 GiB objects<br>Current best trial: 34220_00000 with mse=0.10691251192244274 and parameters={'hidden_dim': 32, 'layer_num': 2, 'dropout': 0.11479529531584376, 'past_seq_len': 15, 'future_seq_len': 1, 'input_feature_num': None, 'output_feature_num': 2, 'selected_features': [], 'batch_size': 64}<br>Result logdir: /tmp/autots_estimator/autots_estimator<br>Number of trials: 4/4 (2 PENDING, 2 RUNNING)<br><table>\n",
       "<thead>\n",
       "<tr><th>Trial name            </th><th>status  </th><th>loc                </th><th style=\"text-align: right;\">  batch_size</th><th style=\"text-align: right;\">  dropout</th><th style=\"text-align: right;\">  hidden_dim</th><th style=\"text-align: right;\">  layer_num</th><th style=\"text-align: right;\">  iter</th><th style=\"text-align: right;\">  total time (s)</th><th style=\"text-align: right;\">  best_mse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>train_func_34220_00000</td><td>RUNNING </td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.114795</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     4</td><td style=\"text-align: right;\">         8.35094</td><td style=\"text-align: right;\">  0.106913</td><td style=\"text-align: right;\">0.106913</td></tr>\n",
       "<tr><td>train_func_34220_00001</td><td>RUNNING </td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.174854</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     2</td><td style=\"text-align: right;\">         6.87402</td><td style=\"text-align: right;\">  0.117832</td><td style=\"text-align: right;\">0.117832</td></tr>\n",
       "<tr><td>train_func_34220_00002</td><td>PENDING </td><td>                   </td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\"> 0.119272</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">      </td><td style=\"text-align: right;\">                </td><td style=\"text-align: right;\">          </td><td style=\"text-align: right;\">        </td></tr>\n",
       "<tr><td>train_func_34220_00003</td><td>PENDING </td><td>                   </td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.123372</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">      </td><td style=\"text-align: right;\">                </td><td style=\"text-align: right;\">          </td><td style=\"text-align: right;\">        </td></tr>\n",
       "</tbody>\n",
       "</table><br><br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "== Status ==<br>Current time: 2022-01-10 22:11:44 (running for 00:00:16.17)<br>Memory usage on this node: 4.7/125.5 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 4.0/4 CPUs, 0/0 GPUs, 0.0/76.72 GiB heap, 0.0/36.87 GiB objects<br>Current best trial: 34220_00000 with mse=0.10215403466869066 and parameters={'hidden_dim': 32, 'layer_num': 2, 'dropout': 0.11479529531584376, 'past_seq_len': 15, 'future_seq_len': 1, 'input_feature_num': None, 'output_feature_num': 2, 'selected_features': [], 'batch_size': 64}<br>Result logdir: /tmp/autots_estimator/autots_estimator<br>Number of trials: 4/4 (1 PENDING, 2 RUNNING, 1 TERMINATED)<br><table>\n",
       "<thead>\n",
       "<tr><th>Trial name            </th><th>status    </th><th>loc                </th><th style=\"text-align: right;\">  batch_size</th><th style=\"text-align: right;\">  dropout</th><th style=\"text-align: right;\">  hidden_dim</th><th style=\"text-align: right;\">  layer_num</th><th style=\"text-align: right;\">  iter</th><th style=\"text-align: right;\">  total time (s)</th><th style=\"text-align: right;\">  best_mse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>train_func_34220_00001</td><td>RUNNING   </td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.174854</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     4</td><td style=\"text-align: right;\">         12.5472</td><td style=\"text-align: right;\">  0.102385</td><td style=\"text-align: right;\">0.105309</td></tr>\n",
       "<tr><td>train_func_34220_00002</td><td>RUNNING   </td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\"> 0.119272</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">      </td><td style=\"text-align: right;\">                </td><td style=\"text-align: right;\">          </td><td style=\"text-align: right;\">        </td></tr>\n",
       "<tr><td>train_func_34220_00003</td><td>PENDING   </td><td>                   </td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.123372</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">      </td><td style=\"text-align: right;\">                </td><td style=\"text-align: right;\">          </td><td style=\"text-align: right;\">        </td></tr>\n",
       "<tr><td>train_func_34220_00000</td><td>TERMINATED</td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.114795</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">         10.7277</td><td style=\"text-align: right;\">  0.102154</td><td style=\"text-align: right;\">0.102154</td></tr>\n",
       "</tbody>\n",
       "</table><br><br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "== Status ==<br>Current time: 2022-01-10 22:11:50 (running for 00:00:22.07)<br>Memory usage on this node: 4.8/125.5 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 4.0/4 CPUs, 0/0 GPUs, 0.0/76.72 GiB heap, 0.0/36.87 GiB objects<br>Current best trial: 34220_00001 with mse=0.09716829421008055 and parameters={'hidden_dim': 64, 'layer_num': 2, 'dropout': 0.1748544092556003, 'past_seq_len': 15, 'future_seq_len': 1, 'input_feature_num': None, 'output_feature_num': 2, 'selected_features': [], 'batch_size': 64}<br>Result logdir: /tmp/autots_estimator/autots_estimator<br>Number of trials: 4/4 (2 RUNNING, 2 TERMINATED)<br><table>\n",
       "<thead>\n",
       "<tr><th>Trial name            </th><th>status    </th><th>loc                </th><th style=\"text-align: right;\">  batch_size</th><th style=\"text-align: right;\">  dropout</th><th style=\"text-align: right;\">  hidden_dim</th><th style=\"text-align: right;\">  layer_num</th><th style=\"text-align: right;\">  iter</th><th style=\"text-align: right;\">  total time (s)</th><th style=\"text-align: right;\">  best_mse</th><th style=\"text-align: right;\">      mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>train_func_34220_00002</td><td>RUNNING   </td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\"> 0.119272</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">     1</td><td style=\"text-align: right;\">         6.32911</td><td style=\"text-align: right;\"> 0.13652  </td><td style=\"text-align: right;\">0.13652  </td></tr>\n",
       "<tr><td>train_func_34220_00003</td><td>RUNNING   </td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.123372</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">      </td><td style=\"text-align: right;\">                </td><td style=\"text-align: right;\">          </td><td style=\"text-align: right;\">         </td></tr>\n",
       "<tr><td>train_func_34220_00000</td><td>TERMINATED</td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.114795</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">        10.7277 </td><td style=\"text-align: right;\"> 0.102154 </td><td style=\"text-align: right;\">0.102154 </td></tr>\n",
       "<tr><td>train_func_34220_00001</td><td>TERMINATED</td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.174854</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">        15.4106 </td><td style=\"text-align: right;\"> 0.0971683</td><td style=\"text-align: right;\">0.0971683</td></tr>\n",
       "</tbody>\n",
       "</table><br><br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "== Status ==<br>Current time: 2022-01-10 22:11:55 (running for 00:00:27.15)<br>Memory usage on this node: 4.8/125.5 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 4.0/4 CPUs, 0/0 GPUs, 0.0/76.72 GiB heap, 0.0/36.87 GiB objects<br>Current best trial: 34220_00001 with mse=0.09716829421008055 and parameters={'hidden_dim': 64, 'layer_num': 2, 'dropout': 0.1748544092556003, 'past_seq_len': 15, 'future_seq_len': 1, 'input_feature_num': None, 'output_feature_num': 2, 'selected_features': [], 'batch_size': 64}<br>Result logdir: /tmp/autots_estimator/autots_estimator<br>Number of trials: 4/4 (2 RUNNING, 2 TERMINATED)<br><table>\n",
       "<thead>\n",
       "<tr><th>Trial name            </th><th>status    </th><th>loc                </th><th style=\"text-align: right;\">  batch_size</th><th style=\"text-align: right;\">  dropout</th><th style=\"text-align: right;\">  hidden_dim</th><th style=\"text-align: right;\">  layer_num</th><th style=\"text-align: right;\">  iter</th><th style=\"text-align: right;\">  total time (s)</th><th style=\"text-align: right;\">  best_mse</th><th style=\"text-align: right;\">      mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>train_func_34220_00002</td><td>RUNNING   </td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\"> 0.119272</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">     2</td><td style=\"text-align: right;\">        12.4922 </td><td style=\"text-align: right;\"> 0.117196 </td><td style=\"text-align: right;\">0.117196 </td></tr>\n",
       "<tr><td>train_func_34220_00003</td><td>RUNNING   </td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.123372</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">     1</td><td style=\"text-align: right;\">         5.71434</td><td style=\"text-align: right;\"> 0.155991 </td><td style=\"text-align: right;\">0.155991 </td></tr>\n",
       "<tr><td>train_func_34220_00000</td><td>TERMINATED</td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.114795</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">        10.7277 </td><td style=\"text-align: right;\"> 0.102154 </td><td style=\"text-align: right;\">0.102154 </td></tr>\n",
       "<tr><td>train_func_34220_00001</td><td>TERMINATED</td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.174854</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">        15.4106 </td><td style=\"text-align: right;\"> 0.0971683</td><td style=\"text-align: right;\">0.0971683</td></tr>\n",
       "</tbody>\n",
       "</table><br><br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "== Status ==<br>Current time: 2022-01-10 22:12:01 (running for 00:00:32.45)<br>Memory usage on this node: 4.8/125.5 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 4.0/4 CPUs, 0/0 GPUs, 0.0/76.72 GiB heap, 0.0/36.87 GiB objects<br>Current best trial: 34220_00001 with mse=0.09716829421008055 and parameters={'hidden_dim': 64, 'layer_num': 2, 'dropout': 0.1748544092556003, 'past_seq_len': 15, 'future_seq_len': 1, 'input_feature_num': None, 'output_feature_num': 2, 'selected_features': [], 'batch_size': 64}<br>Result logdir: /tmp/autots_estimator/autots_estimator<br>Number of trials: 4/4 (2 RUNNING, 2 TERMINATED)<br><table>\n",
       "<thead>\n",
       "<tr><th>Trial name            </th><th>status    </th><th>loc                </th><th style=\"text-align: right;\">  batch_size</th><th style=\"text-align: right;\">  dropout</th><th style=\"text-align: right;\">  hidden_dim</th><th style=\"text-align: right;\">  layer_num</th><th style=\"text-align: right;\">  iter</th><th style=\"text-align: right;\">  total time (s)</th><th style=\"text-align: right;\">  best_mse</th><th style=\"text-align: right;\">      mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>train_func_34220_00002</td><td>RUNNING   </td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\"> 0.119272</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">     3</td><td style=\"text-align: right;\">         18.7851</td><td style=\"text-align: right;\"> 0.105638 </td><td style=\"text-align: right;\">0.105638 </td></tr>\n",
       "<tr><td>train_func_34220_00003</td><td>RUNNING   </td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.123372</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">     2</td><td style=\"text-align: right;\">         11.4454</td><td style=\"text-align: right;\"> 0.122068 </td><td style=\"text-align: right;\">0.122068 </td></tr>\n",
       "<tr><td>train_func_34220_00000</td><td>TERMINATED</td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.114795</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">         10.7277</td><td style=\"text-align: right;\"> 0.102154 </td><td style=\"text-align: right;\">0.102154 </td></tr>\n",
       "<tr><td>train_func_34220_00001</td><td>TERMINATED</td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.174854</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">         15.4106</td><td style=\"text-align: right;\"> 0.0971683</td><td style=\"text-align: right;\">0.0971683</td></tr>\n",
       "</tbody>\n",
       "</table><br><br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "== Status ==<br>Current time: 2022-01-10 22:12:06 (running for 00:00:37.74)<br>Memory usage on this node: 4.8/125.5 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 4.0/4 CPUs, 0/0 GPUs, 0.0/76.72 GiB heap, 0.0/36.87 GiB objects<br>Current best trial: 34220_00001 with mse=0.09716829421008055 and parameters={'hidden_dim': 64, 'layer_num': 2, 'dropout': 0.1748544092556003, 'past_seq_len': 15, 'future_seq_len': 1, 'input_feature_num': None, 'output_feature_num': 2, 'selected_features': [], 'batch_size': 64}<br>Result logdir: /tmp/autots_estimator/autots_estimator<br>Number of trials: 4/4 (2 RUNNING, 2 TERMINATED)<br><table>\n",
       "<thead>\n",
       "<tr><th>Trial name            </th><th>status    </th><th>loc                </th><th style=\"text-align: right;\">  batch_size</th><th style=\"text-align: right;\">  dropout</th><th style=\"text-align: right;\">  hidden_dim</th><th style=\"text-align: right;\">  layer_num</th><th style=\"text-align: right;\">  iter</th><th style=\"text-align: right;\">  total time (s)</th><th style=\"text-align: right;\">  best_mse</th><th style=\"text-align: right;\">      mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>train_func_34220_00002</td><td>RUNNING   </td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\"> 0.119272</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">     4</td><td style=\"text-align: right;\">         25.0777</td><td style=\"text-align: right;\"> 0.105638 </td><td style=\"text-align: right;\">0.119682 </td></tr>\n",
       "<tr><td>train_func_34220_00003</td><td>RUNNING   </td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.123372</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">     3</td><td style=\"text-align: right;\">         17.166 </td><td style=\"text-align: right;\"> 0.107508 </td><td style=\"text-align: right;\">0.107508 </td></tr>\n",
       "<tr><td>train_func_34220_00000</td><td>TERMINATED</td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.114795</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">         10.7277</td><td style=\"text-align: right;\"> 0.102154 </td><td style=\"text-align: right;\">0.102154 </td></tr>\n",
       "<tr><td>train_func_34220_00001</td><td>TERMINATED</td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.174854</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">         15.4106</td><td style=\"text-align: right;\"> 0.0971683</td><td style=\"text-align: right;\">0.0971683</td></tr>\n",
       "</tbody>\n",
       "</table><br><br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "== Status ==<br>Current time: 2022-01-10 22:12:11 (running for 00:00:42.95)<br>Memory usage on this node: 4.8/125.5 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 4.0/4 CPUs, 0/0 GPUs, 0.0/76.72 GiB heap, 0.0/36.87 GiB objects<br>Current best trial: 34220_00001 with mse=0.09716829421008055 and parameters={'hidden_dim': 64, 'layer_num': 2, 'dropout': 0.1748544092556003, 'past_seq_len': 15, 'future_seq_len': 1, 'input_feature_num': None, 'output_feature_num': 2, 'selected_features': [], 'batch_size': 64}<br>Result logdir: /tmp/autots_estimator/autots_estimator<br>Number of trials: 4/4 (2 RUNNING, 2 TERMINATED)<br><table>\n",
       "<thead>\n",
       "<tr><th>Trial name            </th><th>status    </th><th>loc                </th><th style=\"text-align: right;\">  batch_size</th><th style=\"text-align: right;\">  dropout</th><th style=\"text-align: right;\">  hidden_dim</th><th style=\"text-align: right;\">  layer_num</th><th style=\"text-align: right;\">  iter</th><th style=\"text-align: right;\">  total time (s)</th><th style=\"text-align: right;\">  best_mse</th><th style=\"text-align: right;\">      mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>train_func_34220_00002</td><td>RUNNING   </td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\"> 0.119272</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">     4</td><td style=\"text-align: right;\">         25.0777</td><td style=\"text-align: right;\"> 0.105638 </td><td style=\"text-align: right;\">0.119682 </td></tr>\n",
       "<tr><td>train_func_34220_00003</td><td>RUNNING   </td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.123372</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">     4</td><td style=\"text-align: right;\">         22.8931</td><td style=\"text-align: right;\"> 0.100418 </td><td style=\"text-align: right;\">0.100418 </td></tr>\n",
       "<tr><td>train_func_34220_00000</td><td>TERMINATED</td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.114795</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">         10.7277</td><td style=\"text-align: right;\"> 0.102154 </td><td style=\"text-align: right;\">0.102154 </td></tr>\n",
       "<tr><td>train_func_34220_00001</td><td>TERMINATED</td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.174854</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">         15.4106</td><td style=\"text-align: right;\"> 0.0971683</td><td style=\"text-align: right;\">0.0971683</td></tr>\n",
       "</tbody>\n",
       "</table><br><br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "== Status ==<br>Current time: 2022-01-10 22:12:14 (running for 00:00:45.89)<br>Memory usage on this node: 4.4/125.5 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/4 CPUs, 0/0 GPUs, 0.0/76.72 GiB heap, 0.0/36.87 GiB objects<br>Current best trial: 34220_00001 with mse=0.09716829421008055 and parameters={'hidden_dim': 64, 'layer_num': 2, 'dropout': 0.1748544092556003, 'past_seq_len': 15, 'future_seq_len': 1, 'input_feature_num': None, 'output_feature_num': 2, 'selected_features': [], 'batch_size': 64}<br>Result logdir: /tmp/autots_estimator/autots_estimator<br>Number of trials: 4/4 (4 TERMINATED)<br><table>\n",
       "<thead>\n",
       "<tr><th>Trial name            </th><th>status    </th><th>loc                </th><th style=\"text-align: right;\">  batch_size</th><th style=\"text-align: right;\">  dropout</th><th style=\"text-align: right;\">  hidden_dim</th><th style=\"text-align: right;\">  layer_num</th><th style=\"text-align: right;\">  iter</th><th style=\"text-align: right;\">  total time (s)</th><th style=\"text-align: right;\">  best_mse</th><th style=\"text-align: right;\">      mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>train_func_34220_00000</td><td>TERMINATED</td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.114795</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">         10.7277</td><td style=\"text-align: right;\"> 0.102154 </td><td style=\"text-align: right;\">0.102154 </td></tr>\n",
       "<tr><td>train_func_34220_00001</td><td>TERMINATED</td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.174854</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          2</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">         15.4106</td><td style=\"text-align: right;\"> 0.0971683</td><td style=\"text-align: right;\">0.0971683</td></tr>\n",
       "<tr><td>train_func_34220_00002</td><td>TERMINATED</td><td>10.239.44.108:18469</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\"> 0.119272</td><td style=\"text-align: right;\">          32</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">         31.3702</td><td style=\"text-align: right;\"> 0.105033 </td><td style=\"text-align: right;\">0.105033 </td></tr>\n",
       "<tr><td>train_func_34220_00003</td><td>TERMINATED</td><td>10.239.44.108:18471</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\"> 0.123372</td><td style=\"text-align: right;\">          64</td><td style=\"text-align: right;\">          4</td><td style=\"text-align: right;\">     5</td><td style=\"text-align: right;\">         26.8134</td><td style=\"text-align: right;\"> 0.100418 </td><td style=\"text-align: right;\">0.105032 </td></tr>\n",
       "</tbody>\n",
       "</table><br><br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-10 22:12:14,592\tINFO tune.py:626 -- Total run time: 46.02 seconds (45.87 seconds for the tuning loop).\n",
      "WARNING:root:\n",
      "******************************************************************************************************************************************************\n",
      "Nano environment variables ['LD_PRELOAD'] are not set.\n",
      "Please run `source bigdl-nano-init` to initialize them, or you can prefix `bigdl-nano-init` to the command you run.\n",
      "\n",
      "Example:\n",
      "bigdl-nano-init python pytorch-lenet.py --device ipex\n",
      "******************************************************************************************************************************************************\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.33 s, sys: 424 ms, total: 3.76 s\n",
      "Wall time: 47.3 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/liangs/anaconda3/envs/env/lib/python3.6/site-packages/bigdl/nano/pytorch/onnx/onnxrt_inference.py:197: UserWarning: on_fit_start method/property will be replaced.\n",
      "  warnings.warn(f\"{component} method/property will be replaced.\")\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ts_pipeline = autotsest.fit(data=tsdata_train,\n",
    "                            epochs=5,\n",
    "                            batch_size=hp.choice([32, 64]),\n",
    "                            validation_data=tsdata_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'hidden_dim': 64, 'layer_num': 2, 'dropout': 0.1748544092556003, 'past_seq_len': 15, 'future_seq_len': 1, 'input_feature_num': 2, 'output_feature_num': 2, 'selected_features': [], 'batch_size': 64}\n"
     ]
    }
   ],
   "source": [
    "best_config = autotsest.get_best_config()\n",
    "print(best_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `rmse` and `smape` to evaluate the results of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AvgRate rmse is: 77.4952621459961, smape is: 0.1667\n",
      "total rmse is: 70160384000.0, smape is: 0.1688\n"
     ]
    }
   ],
   "source": [
    "rmse, smape = ts_pipeline.evaluate(tsdata_test,\n",
    "                                   multioutput='raw_values',\n",
    "                                   metrics=['rmse', 'smape'])\n",
    "print(f'AvgRate rmse is: {rmse[0][0]}, smape is: {smape[0][0]:.4f}')\n",
    "print(f'total rmse is: {rmse[0][1]}, smape is: {smape[0][1]:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopping orca context\n"
     ]
    }
   ],
   "source": [
    "from bigdl.orca.common import stop_orca_context\n",
    "stop_orca_context()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict\n",
    "yhat = ts_pipeline.predict(tsdata_test)\n",
    "\n",
    "# y_true\n",
    "x_test, y_test = tsdata_test.roll(lookback=best_config['past_seq_len'],\n",
    "                                  horizon=1).to_numpy()\n",
    "unscale_y_test = tsdata_test.unscale_numpy(y_test)\n",
    "\n",
    "# Timeline\n",
    "test_date = tsdata_test.df.StartTime.reset_index(drop=True)[:-best_config['past_seq_len']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot actual and prediction values for `total` KPI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_name = 'total'\n",
    "plot_predict_actual_values(test_date,\n",
    "                           y_pred=yhat[:-1, 0, 0],\n",
    "                           y_test=unscale_y_test[:, 0, 0],\n",
    "                           ylabel=target_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot actual and prediction values for `AvgRate` KPI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_name = 'AvgRate'\n",
    "plot_predict_actual_values(test_date,\n",
    "                           y_pred=yhat[:-1, 0, 1],\n",
    "                           y_test=unscale_y_test[:, 0, 1],\n",
    "                           ylabel=target_name)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "5fd0d186da03a3cc4467b80ade5453b676349bbfc4f1af2284fe352fa0ffaaf6"
  },
  "kernelspec": {
   "display_name": "bigdl",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
